Multi-MCP AI Agent: An Omniscient AI Server Leveraging Model Context Protocol (MCP)

Overview: What is Multi-MCP AI Agent?

The Multi-MCP AI Agent is a sophisticated Agentic AI system designed to leverage multiple Model Context Protocol (MCP) servers for distributed processing and diverse capabilities. It provides a wide array of functionalities, from basic mathematical operations to advanced external service integrations like Google Workspace and web scraping. The agent includes both a Telegram bot interface and Server-Sent Events (SSE) for real-time communication.

🔧 Core Features & MCP Capabilities

Multi-MCP Architecture

The core of the Multi-MCP AI Agent revolves around utilizing multiple MCP servers, each designed to handle specific types of tasks—basic operations, document processing, web integration, and Google Workspace interactions. This architecture ensures that complex tasks can be broken down into manageable parts, allowing for more efficient processing and resource utilization.

Cognitive Modules

The agent implements a comprehensive cognitive architecture consisting of four main modules: perception, decision-making, memory, and action. These modules work together to enable the system to understand input data, make strategic decisions based on context, store historical data, and execute appropriate actions through available tools or services.

External Service Integration

• Google Workspace (Gmail, Google Drive, Google Sheets): Enhanced collaboration and task management capabilities by integrating with these critical Google services.
• Web Scraping & Content Extraction: The system can scrape web content and extract valuable information using advanced data extraction techniques.
• DuckDuckGo Search Integration: Utilizes DuckDuckGo for powerful and privacy-focused search operations.

Real-time Communication

• Telegram Bot Interface: Users can interact with the agent through a Telegram bot, providing a versatile and user-friendly interface.
• Server-Sent Events (SSE): The server pushes real-time updates to clients via SSE, ensuring seamless and efficient communication.

⚙️ MCP Architecture & Protocol Implementation

Project Structure

The Multi-MCP AI Agent is structured with clear separation of concerns. Key components include:

• agent.py: Main entry point for the agent.

• core/: Contains core agent functionalities.

  • context.py: Manages context within operations.
  • loop.py: Orchestrates the main execution loop.
  • session.py: Handles sessions and user states.
  • strategy.py: Facilitates strategic decision making.

• modules/: Houses cognitive modules for various tasks.

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🚀 Getting Started with Installation

Prerequisites

• Python 3.11+
• UV package manager
• Telegram Bot Token (for bot functionality)
• Google Cloud credentials for Google Workspace integration

Steps to Install

1. Clone the repository:

   git clone <repository-url>
   cd <repository-name>
   

2. Create and activate a virtual environment:

   uv venv
   venv\Scripts\activate  # On Mac: source venv/bin/activate
   

3. Install dependencies using UV:

   uv sync
   

4. Set up the environment variables:

   • Create a .env file with Gemini API key and Telegram Bot token.
   • Use Google OAuth to generate credentials.json.

💡 Key Use Cases in AI Workflows

Workflow 1: Real-Time Data Analysis for Financial Trading

• Description: The agent can process real-time financial data using MCP servers, which handle mathematical operations and web scraping. These tasks are integrated with Telegram bot commands that provide up-to-date market analysis.
• MCP Protocol Flow Diagram:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server (Math)]
    B --> D[MCP Server (Web Scrape)]
    C --> E[Real-time Trading System]
    D --> F[Gmail Integration for Alerts]

Workflow 2: Automated Content Extraction and Reporting

• Description: The system can extract content from web pages using the MCP server specialized in web scraping. It then processes this data using tools to create reports or summaries, which are available via Telegram bot commands.
• MCP Protocol Flow Diagram:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server (Web Scrape)]
    C --> D[Data Extraction]
    D --> E[System for Report Generation]

🔌 Integration with MCP Clients

MCP Client Example: Claude Desktop

{
  "mcpServers": {
    "[Math Server]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-math"],
      "env": {
        "API_KEY": "your-api-key"
      }
    },
    "[Web Scrape Server]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-scrape"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

📊 Performance & Compatibility Matrix

MCP Performance and Compatibility

Feature	Math Server	Document Server	Web Server	Google Workspace
Mathematical Operations	✅	❌	❌	❌
Document Indexing	❌	✅	❌	❌
Web Scraping	❌	❌	✅	❌
Google Sheets Operations	❌	❌	❌	✅

🛠️ Advanced Configuration & Security

MCP Server Code Sample

import modelcontextprotocol as mcp

class MathServer(mcp.AbstractMCPClient):
    def __init__(self, api_key):
        self.api_key = api_key

def main():
    server_configs = {
        "math": MathServer("YOUR-MATH-API-KEY"),
        "web_scrape": WebScrapeServer("YOUR-WEB-SCRAPE-API-KEY")
    }
    mcp.connect(server_configs)

if __name__ == "__main__":
    main()

❓ Frequently Asked Questions (FAQ)

Q1: How do I set up the MCP client compatibility matrix?

A1: Refer to the provided table, which outlines the compatibility of each AI application with specific MCP servers. For full support, ensure all required resources and tools are enabled.

Q2: What if my AI application doesn't have full support for MCP clients?

A2: You can still use this system by focusing on compatible aspects or seek alternative MCP solutions tailored to your needs.

Q3: Can I customize the Telegram bot commands for specific tasks?

A3: Yes, you can define custom commands within the agent configuration files. This allows for task-specific interactions with the system using Telegram.

Q4: How do I handle rate-limited web requests with MCP servers?

A4: Implement a rate-limiting mechanism or use MCP server functionalities that support throttling to ensure compliance with external API limits.

Q5: What steps are recommended to secure MCP client access and data privacy?

A5: Follow best practices for securing your MCP clients, such as using environment variables for sensitive information like API keys. Regularly update credentials and review logs for unauthorized access.

👨‍💻 Development & Contribution Guidelines

Interested developers should fork the repository from GitHub, clone it to their local machine, and run uv sync to install dependencies. For contributions, please create an issue or a pull request following the standard guidelines provided in the CONTRIBUTING.md file.

🌐 MCP Ecosystem & Resources

Explore the broader MCP ecosystem for more information on Model Context Protocol and its applications across various AI systems. Additional resources include documentation, community forums, and example projects available on GitHub.

Conclusion

The Multi-MCP AI Agent, with its robust architecture and versatile features, stands out as a powerful tool for developers looking to enhance their AI applications through MCP integration. By leveraging multiple MCP servers, the agent offers unparalleled flexibility and scalability in handling complex tasks across various domains.